{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 17,
   "id": "b69b2748-8379-435e-aa1e-955c8d88ecfe",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Requirement already satisfied: pymysql in /opt/conda/lib/python3.11/site-packages (1.1.1)\n",
      "✅ pymysql 安装成功\n",
      "Requirement already satisfied: pandas in /opt/conda/lib/python3.11/site-packages (2.3.0)\n",
      "Requirement already satisfied: numpy>=1.23.2 in /opt/conda/lib/python3.11/site-packages (from pandas) (2.3.1)\n",
      "Requirement already satisfied: python-dateutil>=2.8.2 in /opt/conda/lib/python3.11/site-packages (from pandas) (2.8.2)\n",
      "Requirement already satisfied: pytz>=2020.1 in /opt/conda/lib/python3.11/site-packages (from pandas) (2023.3.post1)\n",
      "Requirement already satisfied: tzdata>=2022.7 in /opt/conda/lib/python3.11/site-packages (from pandas) (2025.2)\n",
      "Requirement already satisfied: six>=1.5 in /opt/conda/lib/python3.11/site-packages (from python-dateutil>=2.8.2->pandas) (1.16.0)\n",
      "✅ pandas 安装成功\n",
      "Requirement already satisfied: sqlalchemy in /opt/conda/lib/python3.11/site-packages (2.0.22)\n",
      "Requirement already satisfied: typing-extensions>=4.2.0 in /opt/conda/lib/python3.11/site-packages (from sqlalchemy) (4.8.0)\n",
      "Requirement already satisfied: greenlet!=0.4.17 in /opt/conda/lib/python3.11/site-packages (from sqlalchemy) (3.0.0)\n",
      "✅ sqlalchemy 安装成功\n",
      "Collecting matplotlib\n",
      "  Downloading matplotlib-3.10.3-cp311-cp311-manylinux_2_17_aarch64.manylinux2014_aarch64.whl.metadata (11 kB)\n",
      "Collecting contourpy>=1.0.1 (from matplotlib)\n",
      "  Downloading contourpy-1.3.2-cp311-cp311-manylinux_2_17_aarch64.manylinux2014_aarch64.whl.metadata (5.5 kB)\n",
      "Collecting cycler>=0.10 (from matplotlib)\n",
      "  Downloading cycler-0.12.1-py3-none-any.whl.metadata (3.8 kB)\n",
      "Collecting fonttools>=4.22.0 (from matplotlib)\n",
      "  Downloading fonttools-4.58.4-cp311-cp311-manylinux2014_aarch64.manylinux_2_17_aarch64.manylinux_2_28_aarch64.whl.metadata (106 kB)\n",
      "\u001b[2K     \u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m106.6/106.6 kB\u001b[0m \u001b[31m315.0 kB/s\u001b[0m eta \u001b[36m0:00:00\u001b[0ma \u001b[36m0:00:01\u001b[0m\n",
      "\u001b[?25hCollecting kiwisolver>=1.3.1 (from matplotlib)\n",
      "  Downloading kiwisolver-1.4.8-cp311-cp311-manylinux_2_17_aarch64.manylinux2014_aarch64.whl.metadata (6.2 kB)\n",
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      "  Downloading pillow-11.2.1-cp311-cp311-manylinux_2_28_aarch64.whl.metadata (8.9 kB)\n",
      "Collecting pyparsing>=2.3.1 (from matplotlib)\n",
      "  Downloading pyparsing-3.2.3-py3-none-any.whl.metadata (5.0 kB)\n",
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      "\u001b[?25hDownloading contourpy-1.3.2-cp311-cp311-manylinux_2_17_aarch64.manylinux2014_aarch64.whl (313 kB)\n",
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      "\u001b[?25hDownloading cycler-0.12.1-py3-none-any.whl (8.3 kB)\n",
      "Downloading fonttools-4.58.4-cp311-cp311-manylinux2014_aarch64.manylinux_2_17_aarch64.manylinux_2_28_aarch64.whl (5.0 MB)\n",
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      "\u001b[?25hDownloading kiwisolver-1.4.8-cp311-cp311-manylinux_2_17_aarch64.manylinux2014_aarch64.whl (1.4 MB)\n",
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      "\u001b[?25hDownloading pillow-11.2.1-cp311-cp311-manylinux_2_28_aarch64.whl (4.5 MB)\n",
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      "\u001b[?25hDownloading pyparsing-3.2.3-py3-none-any.whl (111 kB)\n",
      "\u001b[2K   \u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m111.1/111.1 kB\u001b[0m \u001b[31m240.5 kB/s\u001b[0m eta \u001b[36m0:00:00\u001b[0ma \u001b[36m0:00:01\u001b[0m\n",
      "\u001b[?25hInstalling collected packages: pyparsing, pillow, kiwisolver, fonttools, cycler, contourpy, matplotlib\n",
      "Successfully installed contourpy-1.3.2 cycler-0.12.1 fonttools-4.58.4 kiwisolver-1.4.8 matplotlib-3.10.3 pillow-11.2.1 pyparsing-3.2.3\n",
      "✅ matplotlib 安装成功\n",
      "Collecting seaborn\n",
      "  Downloading seaborn-0.13.2-py3-none-any.whl.metadata (5.4 kB)\n",
      "Requirement already satisfied: numpy!=1.24.0,>=1.20 in /opt/conda/lib/python3.11/site-packages (from seaborn) (2.3.1)\n",
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      "Requirement already satisfied: fonttools>=4.22.0 in /opt/conda/lib/python3.11/site-packages (from matplotlib!=3.6.1,>=3.4->seaborn) (4.58.4)\n",
      "Requirement already satisfied: kiwisolver>=1.3.1 in /opt/conda/lib/python3.11/site-packages (from matplotlib!=3.6.1,>=3.4->seaborn) (1.4.8)\n",
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      "Requirement already satisfied: pytz>=2020.1 in /opt/conda/lib/python3.11/site-packages (from pandas>=1.2->seaborn) (2023.3.post1)\n",
      "Requirement already satisfied: tzdata>=2022.7 in /opt/conda/lib/python3.11/site-packages (from pandas>=1.2->seaborn) (2025.2)\n",
      "Requirement already satisfied: six>=1.5 in /opt/conda/lib/python3.11/site-packages (from python-dateutil>=2.7->matplotlib!=3.6.1,>=3.4->seaborn) (1.16.0)\n",
      "Downloading seaborn-0.13.2-py3-none-any.whl (294 kB)\n",
      "\u001b[2K   \u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m294.9/294.9 kB\u001b[0m \u001b[31m271.0 kB/s\u001b[0m eta \u001b[36m0:00:00\u001b[0ma \u001b[36m0:00:01\u001b[0m\n",
      "\u001b[?25hInstalling collected packages: seaborn\n",
      "Successfully installed seaborn-0.13.2\n",
      "✅ seaborn 安装成功\n",
      "\n",
      "🎉 所有依赖安装完成！\n"
     ]
    }
   ],
   "source": [
    "# 安装必要的依赖包\n",
    "import subprocess\n",
    "import sys\n",
    "\n",
    "def install_package(package):\n",
    "    \"\"\"安装 Python 包\"\"\"\n",
    "    try:\n",
    "        subprocess.check_call([sys.executable, \"-m\", \"pip\", \"install\", package])\n",
    "        print(f\"✅ {package} 安装成功\")\n",
    "    except subprocess.CalledProcessError:\n",
    "        print(f\"❌ {package} 安装失败\")\n",
    "\n",
    "# 安装连接 Doris 所需的依赖\n",
    "packages = ['pymysql', 'pandas', 'sqlalchemy', 'matplotlib', 'seaborn']\n",
    "for package in packages:\n",
    "    install_package(package)\n",
    "\n",
    "print(\"\\n🎉 所有依赖安装完成！\")\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "id": "f7228343",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "📊 数据库连接配置:\n",
      "主机: host.docker.internal\n",
      "端口: 9030\n",
      "用户: root\n",
      "密码: ****\n"
     ]
    }
   ],
   "source": [
    "# 导入必要的库\n",
    "import pandas as pd\n",
    "import pymysql\n",
    "from sqlalchemy import create_engine\n",
    "import warnings\n",
    "warnings.filterwarnings('ignore')\n",
    "\n",
    "# 数据库连接配置\n",
    "DB_CONFIG = {\n",
    "    'host': 'host.docker.internal',\n",
    "    'port': 9030,\n",
    "    'user': 'root',\n",
    "    'password': 'pass',\n",
    "    'charset': 'utf8mb4'\n",
    "}\n",
    "\n",
    "print(\"📊 数据库连接配置:\")\n",
    "print(f\"主机: {DB_CONFIG['host']}\")\n",
    "print(f\"端口: {DB_CONFIG['port']}\")\n",
    "print(f\"用户: {DB_CONFIG['user']}\")\n",
    "print(f\"密码: {'*' * len(DB_CONFIG['password'])}\")\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "id": "f5c5c148",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "✅ 数据库连接测试成功！\n",
      "📋 数据库版本: 5.7.99\n",
      "📁 可用数据库: ['__internal_schema', 'demo', 'information_schema', 'mysql']\n"
     ]
    }
   ],
   "source": [
    "# 测试数据库连接\n",
    "def test_connection():\n",
    "    \"\"\"测试与 Doris 数据库的连接\"\"\"\n",
    "    try:\n",
    "        # 使用 pymysql 测试连接\n",
    "        connection = pymysql.connect(**DB_CONFIG)\n",
    "        print(\"✅ 数据库连接测试成功！\")\n",
    "        \n",
    "        # 获取数据库版本信息\n",
    "        with connection.cursor() as cursor:\n",
    "            cursor.execute(\"SELECT VERSION();\")\n",
    "            version = cursor.fetchone()\n",
    "            print(f\"📋 数据库版本: {version[0]}\")\n",
    "            \n",
    "            # 显示当前数据库列表\n",
    "            cursor.execute(\"SHOW DATABASES;\")\n",
    "            databases = cursor.fetchall()\n",
    "            print(f\"📁 可用数据库: {[db[0] for db in databases]}\")\n",
    "        \n",
    "        connection.close()\n",
    "        return True\n",
    "        \n",
    "    except Exception as e:\n",
    "        print(f\"❌ 数据库连接失败: {str(e)}\")\n",
    "        return False\n",
    "\n",
    "# 执行连接测试\n",
    "connection_success = test_connection()\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "id": "90633c56",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "✅ SQLAlchemy 引擎创建成功！\n",
      "\n",
      "🔗 现在可以使用 pandas 进行数据库操作了！\n"
     ]
    }
   ],
   "source": [
    "# 创建 SQLAlchemy 引擎用于 pandas 操作\n",
    "def create_engine_connection():\n",
    "    \"\"\"创建 SQLAlchemy 引擎\"\"\"\n",
    "    try:\n",
    "        # 构建连接字符串\n",
    "        connection_string = f\"mysql+pymysql://{DB_CONFIG['user']}:{DB_CONFIG['password']}@{DB_CONFIG['host']}:{DB_CONFIG['port']}\"\n",
    "        \n",
    "        # 创建引擎\n",
    "        engine = create_engine(connection_string, echo=False)\n",
    "        print(\"✅ SQLAlchemy 引擎创建成功！\")\n",
    "        return engine\n",
    "    except Exception as e:\n",
    "        print(f\"❌ SQLAlchemy 引擎创建失败: {str(e)}\")\n",
    "        return None\n",
    "\n",
    "# 创建数据库引擎\n",
    "engine = create_engine_connection()\n",
    "\n",
    "if engine:\n",
    "    print(\"\\n🔗 现在可以使用 pandas 进行数据库操作了！\")\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 29,
   "id": "29d128a1",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "📊 执行省份统计查询...\n",
      "✅ 查询执行成功！\n",
      "📋 省份统计结果 (共 44 个省份):\n"
     ]
    },
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>count</th>\n",
       "      <th>province</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>533</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>1640</td>\n",
       "      <td>上海</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>1025</td>\n",
       "      <td>云南省</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>1779</td>\n",
       "      <td>内蒙古</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>3839</td>\n",
       "      <td>北京</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>2533</td>\n",
       "      <td>台湾省</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>1</td>\n",
       "      <td>吉林</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>957</td>\n",
       "      <td>吉林省</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>2757</td>\n",
       "      <td>四川省</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>435</td>\n",
       "      <td>天津</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>10</th>\n",
       "      <td>696</td>\n",
       "      <td>宁夏</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>11</th>\n",
       "      <td>1</td>\n",
       "      <td>安徽</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>12</th>\n",
       "      <td>1789</td>\n",
       "      <td>安徽省</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>13</th>\n",
       "      <td>2713</td>\n",
       "      <td>山东省</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>14</th>\n",
       "      <td>1</td>\n",
       "      <td>山西</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>15</th>\n",
       "      <td>1111</td>\n",
       "      <td>山西省</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>16</th>\n",
       "      <td>1</td>\n",
       "      <td>广东</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>17</th>\n",
       "      <td>6192</td>\n",
       "      <td>广东省</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>18</th>\n",
       "      <td>2080</td>\n",
       "      <td>广西</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>19</th>\n",
       "      <td>1937</td>\n",
       "      <td>新疆</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>20</th>\n",
       "      <td>2</td>\n",
       "      <td>江苏</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>21</th>\n",
       "      <td>2893</td>\n",
       "      <td>江苏省</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>22</th>\n",
       "      <td>1837</td>\n",
       "      <td>江西省</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>23</th>\n",
       "      <td>2</td>\n",
       "      <td>河北</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>24</th>\n",
       "      <td>1385</td>\n",
       "      <td>河北省</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>25</th>\n",
       "      <td>4</td>\n",
       "      <td>河南</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>26</th>\n",
       "      <td>3353</td>\n",
       "      <td>河南省</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>27</th>\n",
       "      <td>2159</td>\n",
       "      <td>浙江省</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>28</th>\n",
       "      <td>455</td>\n",
       "      <td>海南省</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>29</th>\n",
       "      <td>1920</td>\n",
       "      <td>湖北省</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>30</th>\n",
       "      <td>1670</td>\n",
       "      <td>湖南省</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>31</th>\n",
       "      <td>46</td>\n",
       "      <td>澳门</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>32</th>\n",
       "      <td>1243</td>\n",
       "      <td>甘肃省</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>33</th>\n",
       "      <td>2458</td>\n",
       "      <td>福建省</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>34</th>\n",
       "      <td>401</td>\n",
       "      <td>西藏</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>35</th>\n",
       "      <td>1</td>\n",
       "      <td>贵州</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>36</th>\n",
       "      <td>761</td>\n",
       "      <td>贵州省</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>37</th>\n",
       "      <td>5</td>\n",
       "      <td>辽宁</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>38</th>\n",
       "      <td>1979</td>\n",
       "      <td>辽宁省</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>39</th>\n",
       "      <td>294</td>\n",
       "      <td>重庆</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>40</th>\n",
       "      <td>1797</td>\n",
       "      <td>陕西省</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>41</th>\n",
       "      <td>512</td>\n",
       "      <td>青海省</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>42</th>\n",
       "      <td>4683</td>\n",
       "      <td>香港</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>43</th>\n",
       "      <td>1671</td>\n",
       "      <td>黑龙江省</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "    count province\n",
       "0     533        0\n",
       "1    1640       上海\n",
       "2    1025      云南省\n",
       "3    1779      内蒙古\n",
       "4    3839       北京\n",
       "5    2533      台湾省\n",
       "6       1       吉林\n",
       "7     957      吉林省\n",
       "8    2757      四川省\n",
       "9     435       天津\n",
       "10    696       宁夏\n",
       "11      1       安徽\n",
       "12   1789      安徽省\n",
       "13   2713      山东省\n",
       "14      1       山西\n",
       "15   1111      山西省\n",
       "16      1       广东\n",
       "17   6192      广东省\n",
       "18   2080       广西\n",
       "19   1937       新疆\n",
       "20      2       江苏\n",
       "21   2893      江苏省\n",
       "22   1837      江西省\n",
       "23      2       河北\n",
       "24   1385      河北省\n",
       "25      4       河南\n",
       "26   3353      河南省\n",
       "27   2159      浙江省\n",
       "28    455      海南省\n",
       "29   1920      湖北省\n",
       "30   1670      湖南省\n",
       "31     46       澳门\n",
       "32   1243      甘肃省\n",
       "33   2458      福建省\n",
       "34    401       西藏\n",
       "35      1       贵州\n",
       "36    761      贵州省\n",
       "37      5       辽宁\n",
       "38   1979      辽宁省\n",
       "39    294       重庆\n",
       "40   1797      陕西省\n",
       "41    512      青海省\n",
       "42   4683       香港\n",
       "43   1671     黑龙江省"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\n",
      "🏆 IP地址数量前10的省份:\n"
     ]
    },
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>count</th>\n",
       "      <th>province</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>17</th>\n",
       "      <td>6192</td>\n",
       "      <td>广东省</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>42</th>\n",
       "      <td>4683</td>\n",
       "      <td>香港</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>3839</td>\n",
       "      <td>北京</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>26</th>\n",
       "      <td>3353</td>\n",
       "      <td>河南省</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>21</th>\n",
       "      <td>2893</td>\n",
       "      <td>江苏省</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>2757</td>\n",
       "      <td>四川省</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>13</th>\n",
       "      <td>2713</td>\n",
       "      <td>山东省</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>2533</td>\n",
       "      <td>台湾省</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>33</th>\n",
       "      <td>2458</td>\n",
       "      <td>福建省</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>27</th>\n",
       "      <td>2159</td>\n",
       "      <td>浙江省</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "    count province\n",
       "17   6192      广东省\n",
       "42   4683       香港\n",
       "4    3839       北京\n",
       "26   3353      河南省\n",
       "21   2893      江苏省\n",
       "8    2757      四川省\n",
       "13   2713      山东省\n",
       "5    2533      台湾省\n",
       "33   2458      福建省\n",
       "27   2159      浙江省"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\n",
      "📈 统计信息:\n",
      "• 总省份数: 44\n",
      "• 总IP地址数: 63,551\n",
      "• 平均每省IP数: 1444\n",
      "• 最大值: 6,192 (广东省)\n",
      "• 最小值: 1 (吉林)\n"
     ]
    }
   ],
   "source": [
    "# 导入必要的库\n",
    "import pandas as pd\n",
    "import matplotlib.pyplot as plt\n",
    "import seaborn as sns\n",
    "import numpy as np\n",
    "from IPython.display import display\n",
    "\n",
    "# 执行省份统计查询\n",
    "def execute_province_count_query():\n",
    "    \"\"\"执行省份统计查询\"\"\"\n",
    "    try:\n",
    "        # 创建数据库连接字符串\n",
    "        connection_string = f\"mysql+pymysql://{DB_CONFIG['user']}:{DB_CONFIG['password']}@{DB_CONFIG['host']}:{DB_CONFIG['port']}/demo\"\n",
    "        query_engine = create_engine(connection_string, echo=False)\n",
    "        \n",
    "        # 执行指定的查询\n",
    "        query = \"\"\"\n",
    "        select count(*) as count, province from ip_geo ig\n",
    "        group by province;\n",
    "        \"\"\"\n",
    "        \n",
    "        # 使用 pandas 执行查询\n",
    "        result_df = pd.read_sql(query, query_engine)\n",
    "        \n",
    "        # 设置 pandas 显示选项\n",
    "        pd.set_option('display.max_columns', None)\n",
    "        pd.set_option('display.width', None)\n",
    "        pd.set_option('display.max_colwidth', 50)\n",
    "        pd.set_option('display.precision', 2)\n",
    "        \n",
    "        return result_df\n",
    "        \n",
    "    except Exception as e:\n",
    "        print(f\"❌ 查询执行失败: {str(e)}\")\n",
    "        return None\n",
    "\n",
    "# 执行查询并显示结果\n",
    "print(\"📊 执行省份统计查询...\")\n",
    "province_count_result = execute_province_count_query()\n",
    "\n",
    "# 显示查询结果\n",
    "if province_count_result is not None and not province_count_result.empty:\n",
    "    print(\"✅ 查询执行成功！\")\n",
    "    print(f\"📋 省份统计结果 (共 {len(province_count_result)} 个省份):\")\n",
    "    display(province_count_result)\n",
    "    \n",
    "    # 按计数排序显示前10名\n",
    "    top_provinces = province_count_result.sort_values('count', ascending=False).head(10)\n",
    "    print(f\"\\n🏆 IP地址数量前10的省份:\")\n",
    "    display(top_provinces)\n",
    "    \n",
    "    print(f\"\\n📈 统计信息:\")\n",
    "    print(f\"• 总省份数: {len(province_count_result)}\")\n",
    "    print(f\"• 总IP地址数: {province_count_result['count'].sum():,}\")\n",
    "    print(f\"• 平均每省IP数: {province_count_result['count'].mean():.0f}\")\n",
    "    print(f\"• 最大值: {province_count_result['count'].max():,} ({province_count_result.loc[province_count_result['count'].idxmax(), 'province']})\")\n",
    "    print(f\"• 最小值: {province_count_result['count'].min():,} ({province_count_result.loc[province_count_result['count'].idxmin(), 'province']})\")\n",
    "else:\n",
    "    print(\"⚠️ 查询没有返回任何数据\")\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 30,
   "id": "b2daebe5",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "🔧 正在配置中文字体...\n",
      "✅ 尝试安装系统中文字体\n",
      "⚠️ 字体配置警告: module 'matplotlib.font_manager' has no attribute '_rebuild'\n",
      "🎨 创建省份IP地址统计柱状图...\n",
      "⚠️ 检测到中文字体可能显示异常，将使用英文标签\n"
     ]
    },
    {
     "data": {
      "image/png": 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8sXH+/Pkc9x0Ax8KVfgBQCFxcXPTwww/r+++/N78pmZlbJ47+66+/ctx22hVQksxvkUlSs2bNMm1zu2rXrm1+Q9XFxcX89lhMTMwdbzsnf/75p/n/OU2unTa/wPXr19W+fXv5+vqqUqVKeuKJJ/TTTz/d1ut37tw5199eTS/98fD19VWtWrXMn9Nf0VnQsjpH6tevb36z+Z9//sn0m40PPvig+f+lS5c2/z+vxz3tm6Bpj6y+ZZqZmjVrmle9fvHFF7rnnntUrFgxBQcH68MPP1RCQkKutpPVfpAy/31Jf4zSt09/5WlWHn744QzLQkND1b17d23dulV///23UlJSMrTJbL/WqlVLxYsXl3Tz285pmjRpYn4Ls0yZMtluAwAAwJlcunTJnKPM29tb9evXN9flNA4qVaqUatSokWn7/PyMnnYFlqQMn1fT390hfbu8cnd31xNPPKFNmzbJ1dU1w/rMPpPmdWzwxBNPmG1Wr16t1NRUc86wGjVqZPhcnZ0WLVqYd+nIbGzx999/m/OkeXh4qH379pluJyIiwrxiKzo6Wg888ID5SD+f2a+//iqpYMaIaW49Bn///bd5biYmJqp9+/Zm3+bNm5ehb+nVrFnTaswoWR+v9u3bW81xmVW7kJAQ8zVbt26t69evS5KuXLmS6d8Y7nTMd/XqVd1///367LPPdPr06UzHZ2nbSv871rhxY7m5uZk/ZzbOSp/ru+++szrWaVfTGoZhXvWbld9//92cw71Bgwa69957Jd280jDN8uXLs3x+Tvvoxx9/VNu2bfXNN98oOjo60zn8ctqf999/v3k168qVK5WSkqJ169aZ6x977DHz/4cNGyZJCg8PV6NGjVS0aFHVrVtX48eP1/nz57N9nZ49e5oZnnvuOZUvX16lSpVSly5dtGrVqmyfC8AxUPQDgAIQFBSkPXv2aO/evTpy5IhiYmL07bffqmrVqtk+r1ixYqpevbr58759+267DzndkuHWgkNmk2und+stIm6nCHa70u+HoKCgbNt27NhR+/bt01NPPaVGjRrJ29tbUVFRWrp0qR588MHbGsjfejue25XZMUm/LP0xyel4FJb0xz39MU8bZOdWq1atrB5pt1HJDRcXF23cuFEfffSROnfurMqVK+vGjRs6cOCAJkyYoGeffTZPfclMTr8veb3FSWbnzJw5c8z/HzJkiLZs2aI9e/aoQ4cO5vLU1NQMz0tf6HZx+b+PbmmFwFvl9dgAAAA4sls/p+X1c1tB3cou/djvwoULVuuio6PN/8/L7SW7dOlijjPDw8MVExOjL774wqoIkV5+jGPatWunChUqSLpZ9Nu7d6/Z/wEDBuRpW3kZW6RNC3An0m7vmZ9jxLweg5z6ll5+jTvz+rp3OuZbs2aNeevK2rVra+XKldqzZ4/VNCOZjXPy83cvs1zppd3SVpKOHz9unl9NmjQxl+/du9fqC8fp5bSP5s+fb04j8fDDD2vjxo3as2ePeYtTKfN9cKu0Yt7Fixe1ZcsWs+hXr1498xa8kvTWW29p+fLl+te//qVatWrJYrHo119/1YwZM9SxY8dMi45p/P39dfjwYf373/9Wq1atVLp0af3zzz/atGmTHn30Uat9BcAxUfQDgALg6+urVq1aqWXLlmrUqFG2V/fdql+/fub/T58+PdNv4l28eNH8BmTaN9Skm98uS5N2D/hb26QVEP73v/+ZH0ojIyNz/GZcbqV9cM/NB9rcWLt2rXbt2mX+nH7/ZMYwDAUHB+vTTz/VkSNHdPXqVX300UeSbs4HkH5+xdz29XYHI+mPx5UrV3Ty5Enz57TibvqCTtrgOTU1VVu3bs10m+mLPrndx1mdIz///LP5rc+SJUuqbNmyudpeYTMMQ8WKFdP48eP13Xff6cyZM7p48aL5B5K0+SGk7PdPVvvh1p/T2t1zzz3mskOHDpn/v3///hz7nNk5k34AOXv2bHXo0EH3339/lgNLAAAAZK5s2bLmfNNxcXFW801nNQ5Kc/nyZUVERGTaPv0XMO/U/fffb342PXr0qOLj4yVJ8fHxOnr0qCTJ1dU1V3eRSOPn52eOMxs2bCgvL69s22f2mTSvYwMXFxdz/r0TJ07o7bffNp+T/irA/FCmTBmzuBIfH69t27Zl2q5GjRpmtnvuuUfJyckyDMPqkZiYqDfffFNS3saIOcnpGKTPUKxYMV29ejVD31JSUhQSEpJh2zkdr23btmU6B/it7V5//fUMr2kYhuLi4jJcSZhb2Y2z0o9nxo4dq0cffVStWrUyz/n00o+xjh49avXF18zGWelzDR48OMtcnTp1yrb/2V3Fl8YwjEznAc2N9Ptg6tSp6tKli1q1apWh4J+TQYMGmVc/vvPOO+a/bemv8kvTv39/ffXVV/rtt9909epV9e3bV9LN3+Xs7vZkGIaqVKmi9957T3v27NHff/9tNd5NP74G4JgK7zINAECuvPjii1q6dKnOnj2rmJgYNW/eXC+++KIaNGigq1evateuXQoJCdGuXbtUqlQpPf744+bk4WPHjtXVq1dlsVj08ssvm9tM/wGxRo0aOnz4sG7cuKHHH39crVu31rx58zK91eDtKFmypC5fvqy//vpLS5cuVZUqVVSuXLlcX9l18eJF7d27V5cvX9bWrVv16aefmuu6d+9udVVUZp555hmdP39eHTp0UKVKlVSkSBHt2bPHXJ/+ViN32tecLF++XLVr11ajRo00Z84c89uHjRo1MidIT39roaefflrDhw/X+vXrs/yQnv4bhl9//bWqVasmNzc33XfffVne6uXxxx83vyE4efJkeXh4qEyZMnrjjTfMNv369bPbCbv//PNPtW/fXo8++qjq1q2rcuXK6fTp0+Yth249pmlu3T8dO3ZU6dKl9b///U8//fSTxo0bp27dumnjxo3mbX3KlCljnmO9evXSiRMnJEnjxo3Te++9p+vXr2vSpEm3laNKlSrmcZ08ebI6deqkL774wnwNAAAA5E5aIWr+/PmSbl5x9vrrr+uff/7R66+/brbL7A/l0s3Px6+++qqioqL08ccfm8t79uyZ42t/9913iouLs/py5vXr17V69WpJN6/wa9q0qfz9/dWzZ0+tWbNGV65c0WOPPaYnn3xSISEhio2NlSQ98sgjhXJ1V3q3MzZ44oknNH36dEkyv5zYvHlzq7FMfnBxcdHjjz+uuXPnmn197bXXVLt2bf3xxx9at26dNm7caN6KcOPGjTp16pR69OihYcOGycfHR2fOnNHRo0cVGhqq/fv3q2rVqnkaI+ZHhscee0zz5s3TtWvX1LFjRz3zzDMqU6aMoqKi9PPPPys0NFSLFi1SmzZtctxex44d5efnp4sXL+r06dPq2LGjxo0bJ09PT+3du1elS5fWSy+9pL59++rll19WQkKC3nvvPVksFgUHB+v69es6ffq0du7cqRs3bmT55dKcZDfOqlKlirlu0aJFql69uiIiIqwKxGkaN26sSpUq6dy5c/rrr780aNAgDRgwQJs3b9aBAwcytO/QoYPKli2rS5cuacmSJSpVqpQ6dOiglJQURUZGat++fQoPD892TBUeHm7eTrVKlSp68cUXrdZHR0frnXfekXRzDD9+/Pi87Zz/v900U6dO1eDBg/Xdd99p8+bNedqOn5+fHn74Ya1Zs8bqjkdphfc0aV8wb9asmSpWrKirV69a7YPszunly5dr/vz56tWrl6pVqyZfX1/t2LEjV88F4CAKY+JAAHAm6SewHjx4sLk8JCTEXP7ggw/e0Wv88ssvRvXq1bOdKPzo0aOGYRhGfHy88cADD2TZrnXr1kZCQoK57QULFmRoU6xYMSMgIMD8+fTp04ZhWE8qfmumzCZpNwzD6NOnT4btp99PmcnNxOjdunUzYmNjc+zDsGHDstyGl5eXcerUqVz1Nf3xfP311zP0Oat9kz5LzZo1M2y/SJEi5gTahmEYJ06cMFxcXDK0q127dqYTpR87dsyctD79I+2YZbZPUlNTjX79+mW5X2rXrm1cvnzZbP/ggw9m2O6t2TKbvD2r/ZPZx42sfl8y2+/nzp3L9twYOXJkrvfP2rVrDTc3t0y34+bmZjW5+j///GO1P9MeDRs2zPHYZ7Z/Vq1alWFbnp6eVhPKp50bWZ1fO3fuzPHfn8zOVwAAAEeV/vNY+s/R//vf/6w+M9/66N+/v5Gammq2T1teqlQpq7FP2qNDhw5W7XPTn8we6T+jnT17NtPXkmRUrlzZiIqKyvH1shp/5qZ9Zp9J8zo2SFOnTh2rdrNmzcp236TJ6vOrYfzfMalSpYq5LCYmxurzdvpH+nZnzpzJct/eOgbIyxgxp32am2Pwzz//GA0aNMi2bzl97k9v48aNhoeHR6bbSf/Z/7PPPst0bJnZ9gcPHpzp71VW44rsxlmxsbFG+fLlM6xr2bJlpvvt66+/znRb6fdZ+nN3w4YNWea/9bzIzMsvv2y2feGFFzKsT0lJMUqXLm22iYiIMAwj6795ZLbvDh48mCGTxWIxgoODM82UXd/Xr19vtZ1mzZplaHPPPfdkuT/q1q1rJCcnZ9nXL774Ittzc/ny5dnuTwD2j9t7AoAdqlu3ro4dO6bp06erVatWKlWqlNzd3VWpUiV16tRJ//nPf1S3bl1JNyc437p1q9577z3z9iKenp5q0KCBpk6dqi1btsjd3d3c9vDhwzVx4kT5+fnJy8tL7dq10549e6xus3En5syZo0cfffSObhXp4uIiHx8f3XvvvfrXv/6lb7/9Vt9++22ubpM6YMAADR48WLVq1ZKvr69cXV3l5+enXr16ac+ePVa37MmPvmZn4sSJ+uCDD1S1alW5u7urUaNGWr9+vdU3OuvUqaOlS5eqRo0acnd3V/369fXVV19leRvTBg0aaMmSJapTp06WV/bdymKxaNmyZZo/f76aNWumokWLysPDQ/fee69efvllHThwIMOcjfakVKlSev311/Xggw+qfPnycnNzk5eXlxo2bKi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      "text/plain": [
       "<Figure size 1800x1200 with 4 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\n",
      "📊 Chart Description:\n",
      "• Top-left: Top 15 provinces with most IP addresses\n",
      "• Top-right: Bottom 15 provinces with least IP addresses\n",
      "• Bottom-left: Overall distribution of IP counts, red line=mean, green line=median\n",
      "• Bottom-right: Percentage analysis of top 10 provinces\n",
      "\n",
      "🔍 Key Insights:\n",
      "• Top 5 provinces account for 33.0% of total IP addresses\n",
      "• Province with most IPs: 广东省 (6,192 IPs)\n",
      "• Province with least IPs: 广东 (1 IPs)\n",
      "• IP count difference ratio: 6192x\n"
     ]
    }
   ],
   "source": [
    "# 设置字体支持（解决中文显示问题）\n",
    "import matplotlib.font_manager as fm\n",
    "\n",
    "# 检查可用字体并设置最佳中文字体\n",
    "def setup_chinese_fonts():\n",
    "    \"\"\"设置中文字体支持\"\"\"\n",
    "    # 获取系统可用字体\n",
    "    available_fonts = [f.name for f in fm.fontManager.ttflist]\n",
    "    \n",
    "    # 中文字体优先级列表\n",
    "    chinese_fonts = [\n",
    "        'SimHei',           # 黑体\n",
    "        'Microsoft YaHei',  # 微软雅黑\n",
    "        'Arial Unicode MS', # Arial Unicode MS\n",
    "        'Noto Sans CJK SC', # Noto Sans\n",
    "        'WenQuanYi Micro Hei',  # 文泉驿微米黑\n",
    "        'DejaVu Sans'       # DejaVu Sans (备选)\n",
    "    ]\n",
    "    \n",
    "    # 找到第一个可用的中文字体\n",
    "    selected_font = 'DejaVu Sans'  # 默认字体\n",
    "    for font in chinese_fonts:\n",
    "        if font in available_fonts:\n",
    "            selected_font = font\n",
    "            break\n",
    "    \n",
    "    print(f\"🔤 选择字体: {selected_font}\")\n",
    "    \n",
    "    # 设置matplotlib字体\n",
    "    plt.rcParams['font.sans-serif'] = [selected_font, 'DejaVu Sans']\n",
    "    plt.rcParams['axes.unicode_minus'] = False\n",
    "    plt.rcParams['font.size'] = 10\n",
    "    \n",
    "    return selected_font\n",
    "\n",
    "# 安装和配置中文字体\n",
    "try:\n",
    "    # 首先尝试安装字体包\n",
    "    import subprocess\n",
    "    import sys\n",
    "    \n",
    "    print(\"🔧 正在配置中文字体...\")\n",
    "    \n",
    "    # 尝试安装字体包（适用于Ubuntu/Debian系统）\n",
    "    try:\n",
    "        subprocess.run(['apt-get', 'update'], capture_output=True, check=False)\n",
    "        subprocess.run(['apt-get', 'install', '-y', 'fonts-wqy-microhei', 'fonts-wqy-zenhei'], \n",
    "                      capture_output=True, check=False)\n",
    "        print(\"✅ 尝试安装系统中文字体\")\n",
    "    except:\n",
    "        pass\n",
    "    \n",
    "    # 清除matplotlib字体缓存\n",
    "    try:\n",
    "        import shutil\n",
    "        import os\n",
    "        cache_dir = fm.get_cachedir()\n",
    "        if os.path.exists(cache_dir):\n",
    "            shutil.rmtree(cache_dir)\n",
    "        print(\"🧹 清除字体缓存\")\n",
    "    except:\n",
    "        pass\n",
    "    \n",
    "    # 重新构建字体列表\n",
    "    fm._rebuild()\n",
    "    \n",
    "    # 设置字体\n",
    "    selected_font = setup_chinese_fonts()\n",
    "    \n",
    "except Exception as e:\n",
    "    print(f\"⚠️ 字体配置警告: {e}\")\n",
    "    # 使用备选方案\n",
    "    plt.rcParams['font.sans-serif'] = ['DejaVu Sans']\n",
    "    plt.rcParams['axes.unicode_minus'] = False\n",
    "\n",
    "# 创建省份IP地址统计柱状图\n",
    "def create_province_count_charts():\n",
    "    \"\"\"基于省份统计查询结果创建柱状图\"\"\"\n",
    "    if 'province_count_result' not in globals() or province_count_result is None or province_count_result.empty:\n",
    "        print(\"⚠️ 没有省份统计数据可供可视化\")\n",
    "        return\n",
    "    \n",
    "    # 设置图表样式\n",
    "    plt.style.use('default')\n",
    "    sns.set_palette(\"viridis\")\n",
    "    \n",
    "    # 检测中文字体是否正常工作\n",
    "    test_fig, test_ax = plt.subplots(figsize=(1, 1))\n",
    "    test_ax.text(0.5, 0.5, '测试中文', fontsize=12)\n",
    "    test_fig.canvas.draw()\n",
    "    \n",
    "    # 检查是否显示为方框（简单检测）\n",
    "    use_english = False\n",
    "    try:\n",
    "        # 如果字体是DejaVu Sans，很可能中文会显示异常\n",
    "        current_font = plt.rcParams['font.sans-serif'][0]\n",
    "        if current_font == 'DejaVu Sans':\n",
    "            use_english = True\n",
    "            print(\"⚠️ 检测到中文字体可能显示异常，将使用英文标签\")\n",
    "    except:\n",
    "        use_english = True\n",
    "    \n",
    "    plt.close(test_fig)\n",
    "    \n",
    "    # 根据字体情况选择标题和标签\n",
    "    if use_english:\n",
    "        main_title = 'IP Address Count Analysis by Province'\n",
    "        chart_titles = [\n",
    "            'Top 15 Provinces by IP Count',\n",
    "            'Bottom 15 Provinces by IP Count', \n",
    "            'IP Count Distribution Histogram',\n",
    "            'Top 10 Provinces Percentage Analysis'\n",
    "        ]\n",
    "        axis_labels = {\n",
    "            'province': 'Province',\n",
    "            'count': 'IP Count',\n",
    "            'frequency': 'Frequency'\n",
    "        }\n",
    "        legend_labels = {\n",
    "            'mean': 'Mean',\n",
    "            'median': 'Median',\n",
    "            'others': 'Others'\n",
    "        }\n",
    "    else:\n",
    "        main_title = '各省份IP地址数量统计分析'\n",
    "        chart_titles = [\n",
    "            'IP地址数量前15名省份',\n",
    "            'IP地址数量后15名省份',\n",
    "            'IP地址数量分布直方图', \n",
    "            'IP地址数量占比分析'\n",
    "        ]\n",
    "        axis_labels = {\n",
    "            'province': '省份',\n",
    "            'count': 'IP地址数量',\n",
    "            'frequency': '省份数量'\n",
    "        }\n",
    "        legend_labels = {\n",
    "            'mean': '平均值',\n",
    "            'median': '中位数',\n",
    "            'others': '其他省份'\n",
    "        }\n",
    "    \n",
    "    # 创建图表布局\n",
    "    fig, axes = plt.subplots(2, 2, figsize=(18, 12))\n",
    "    fig.suptitle(main_title, fontsize=16, fontweight='bold')\n",
    "    \n",
    "    # 按计数排序数据\n",
    "    sorted_data = province_count_result.sort_values('count', ascending=False)\n",
    "    \n",
    "    # 图1: 前15名省份柱状图\n",
    "    ax1 = axes[0, 0]\n",
    "    top_15 = sorted_data.head(15)\n",
    "    bars1 = ax1.bar(range(len(top_15)), top_15['count'], \n",
    "                    color=plt.cm.plasma(np.linspace(0, 1, len(top_15))))\n",
    "    ax1.set_title(chart_titles[0], fontsize=12, fontweight='bold')\n",
    "    ax1.set_xlabel(axis_labels['province'], fontsize=10)\n",
    "    ax1.set_ylabel(axis_labels['count'], fontsize=10)\n",
    "    ax1.set_xticks(range(len(top_15)))\n",
    "    ax1.set_xticklabels(top_15['province'], rotation=45, ha='right')\n",
    "    \n",
    "    # 在柱子上显示数值\n",
    "    for i, (bar, value) in enumerate(zip(bars1, top_15['count'])):\n",
    "        height = bar.get_height()\n",
    "        ax1.text(bar.get_x() + bar.get_width()/2., height + max(top_15['count'])*0.01,\n",
    "                f'{value:,}', ha='center', va='bottom', fontsize=8)\n",
    "    \n",
    "    # 图2: 后15名省份柱状图\n",
    "    ax2 = axes[0, 1]\n",
    "    bottom_15 = sorted_data.tail(15)\n",
    "    bars2 = ax2.bar(range(len(bottom_15)), bottom_15['count'], \n",
    "                    color=plt.cm.cool(np.linspace(0, 1, len(bottom_15))))\n",
    "    ax2.set_title(chart_titles[1], fontsize=12, fontweight='bold')\n",
    "    ax2.set_xlabel(axis_labels['province'], fontsize=10)\n",
    "    ax2.set_ylabel(axis_labels['count'], fontsize=10)\n",
    "    ax2.set_xticks(range(len(bottom_15)))\n",
    "    ax2.set_xticklabels(bottom_15['province'], rotation=45, ha='right')\n",
    "    \n",
    "    # 在柱子上显示数值\n",
    "    for i, (bar, value) in enumerate(zip(bars2, bottom_15['count'])):\n",
    "        height = bar.get_height()\n",
    "        ax2.text(bar.get_x() + bar.get_width()/2., height + max(bottom_15['count'])*0.01,\n",
    "                f'{value:,}', ha='center', va='bottom', fontsize=8)\n",
    "    \n",
    "    # 图3: IP地址数量分布直方图\n",
    "    ax3 = axes[1, 0]\n",
    "    ax3.hist(province_count_result['count'], bins=20, color='lightblue', \n",
    "             alpha=0.7, edgecolor='black')\n",
    "    ax3.set_title(chart_titles[2], fontsize=12, fontweight='bold')\n",
    "    ax3.set_xlabel(axis_labels['count'], fontsize=10)\n",
    "    ax3.set_ylabel(axis_labels['frequency'], fontsize=10)\n",
    "    \n",
    "    # 添加统计线\n",
    "    mean_count = province_count_result['count'].mean()\n",
    "    median_count = province_count_result['count'].median()\n",
    "    ax3.axvline(mean_count, color='red', linestyle='--', linewidth=2, label=f'{legend_labels[\"mean\"]}: {mean_count:.0f}')\n",
    "    ax3.axvline(median_count, color='green', linestyle='--', linewidth=2, label=f'{legend_labels[\"median\"]}: {median_count:.0f}')\n",
    "    ax3.legend()\n",
    "    \n",
    "    # 图4: 饼图显示前10名省份占比\n",
    "    ax4 = axes[1, 1]\n",
    "    top_10 = sorted_data.head(10)\n",
    "    others_count = sorted_data.tail(len(sorted_data) - 10)['count'].sum() if len(sorted_data) > 10 else 0\n",
    "    \n",
    "    # 准备饼图数据\n",
    "    pie_data = list(top_10['count'])\n",
    "    pie_labels = list(top_10['province'])\n",
    "    if others_count > 0:\n",
    "        pie_data.append(others_count)\n",
    "        pie_labels.append(legend_labels['others'])\n",
    "    \n",
    "    # 生成颜色\n",
    "    colors = plt.cm.Set3(np.linspace(0, 1, len(pie_data)))\n",
    "    \n",
    "    wedges, texts, autotexts = ax4.pie(pie_data, labels=pie_labels, autopct='%1.1f%%',\n",
    "                                       startangle=90, colors=colors)\n",
    "    ax4.set_title(chart_titles[3], fontsize=12, fontweight='bold')\n",
    "    \n",
    "    # 美化饼图文本\n",
    "    for autotext in autotexts:\n",
    "        autotext.set_color('white')\n",
    "        autotext.set_fontweight('bold')\n",
    "        autotext.set_fontsize(8)\n",
    "    \n",
    "    # 调整布局\n",
    "    plt.tight_layout()\n",
    "    plt.show()\n",
    "    \n",
    "    # 显示图表说明\n",
    "    print(\"\\n📊 Chart Description:\" if use_english else \"\\n📊 图表说明:\")\n",
    "    if use_english:\n",
    "        print(\"• Top-left: Top 15 provinces with most IP addresses\")\n",
    "        print(\"• Top-right: Bottom 15 provinces with least IP addresses\") \n",
    "        print(\"• Bottom-left: Overall distribution of IP counts, red line=mean, green line=median\")\n",
    "        print(\"• Bottom-right: Percentage analysis of top 10 provinces\")\n",
    "    else:\n",
    "        print(\"• 左上图: 显示IP地址数量最多的前15个省份\")\n",
    "        print(\"• 右上图: 显示IP地址数量最少的后15个省份\") \n",
    "        print(\"• 左下图: IP地址数量的整体分布情况，红线为平均值，绿线为中位数\")\n",
    "        print(\"• 右下图: 前10名省份的IP地址数量占比分析\")\n",
    "    \n",
    "    # 输出关键洞察\n",
    "    total_ip = province_count_result['count'].sum()\n",
    "    top_5_total = sorted_data.head(5)['count'].sum()\n",
    "    top_5_percent = (top_5_total / total_ip) * 100\n",
    "    \n",
    "    print(f\"\\n🔍 Key Insights:\" if use_english else f\"\\n🔍 关键洞察:\")\n",
    "    if use_english:\n",
    "        print(f\"• Top 5 provinces account for {top_5_percent:.1f}% of total IP addresses\")\n",
    "        print(f\"• Province with most IPs: {sorted_data.iloc[0]['province']} ({sorted_data.iloc[0]['count']:,} IPs)\")\n",
    "        print(f\"• Province with least IPs: {sorted_data.iloc[-1]['province']} ({sorted_data.iloc[-1]['count']:,} IPs)\")\n",
    "        print(f\"• IP count difference ratio: {sorted_data.iloc[0]['count'] / sorted_data.iloc[-1]['count']:.0f}x\")\n",
    "    else:\n",
    "        print(f\"• 前5名省份占总IP地址数量的 {top_5_percent:.1f}%\")\n",
    "        print(f\"• 最多的省份({sorted_data.iloc[0]['province']})有 {sorted_data.iloc[0]['count']:,} 个IP地址\")\n",
    "        print(f\"• 最少的省份({sorted_data.iloc[-1]['province']})有 {sorted_data.iloc[-1]['count']:,} 个IP地址\")\n",
    "        print(f\"• 省份间IP地址数量差异达 {sorted_data.iloc[0]['count'] / sorted_data.iloc[-1]['count']:.0f} 倍\")\n",
    "\n",
    "# 创建柱状图\n",
    "print(\"🎨 创建省份IP地址统计柱状图...\")\n",
    "create_province_count_charts()\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "57b1ac1a-dcb6-4427-ba93-66dce8aeddb6",
   "metadata": {},
   "outputs": [],
   "source": []
  }
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